Active Protection Scheme of DNN Intellectual Property Rights Based on Feature Layer Selection and Hyperchaotic Mapping

Deep neural network (DNN) models have achieved remarkable performance across diverse tasks, leading to widespread commercial adoption. However, training high-accuracy models demands extensive data, substantial computational resources, and significant time investment, making them valuable assets vulnerable to unauthorized exploitation. To address this issue, this paper proposes an intellectual property (IP) protection framework for DNN models based on feature layer selection and hyper-chaotic mapping. Firstly, a sensitivity-based importance evaluation algorithm is used to identify the key feature layers for encryption, effectively protecting the core components of the model. Next, the L1 regularization criterion is applied to further select high-weight features that significantly impact the model’s performance, ensuring that the encryption process minimizes performance loss. Finally, a dual-layer encryption mechanism is designed, introducing perturbations into the weight values and utilizing hyperchaotic mapping to disrupt channel information, further enhancing the model’s security. Experimental results demonstrate that encrypting only a small subset of parameters effectively reduces model accuracy to random-guessing levels while ensuring full recoverability. The scheme exhibits strong robustness against model pruning and fine-tuning attacks and maintains consistent performance across multiple datasets, providing an efficient and practical solution for authorization-based DNN IP protection.